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To develop and externally validate a two-stage machine learning framework that integrates polygenic risk and clinical variables for early identification of individuals at risk of developing type 2 diabetes.
We conducted a prospective prediction study using data from the All of Us Research Program for model development and the UK Biobank for external validation. Two models were constructed. Stage 1 used gradient boosted decision trees (XGBoost) with cross validation, automated hyperparameter optimisation and class weighting to predict 5-year incident type 2 diabetes using demographic, clinical and polygenic predictors. Stage 2 incorporated glycated haemoglobin or fasting glucose measurements to refine risk estimates. Model interpretation used SHapley Additive exPlanations values and permutation importance, and logistic regression and random forest models served as comparators. Discrimination of all models was compared using the DeLong test.
The Stage 1 model achieved an area under the receiver operating characteristic curve (AUROC) of 0.81 in All of Us and 0.82 in UK Biobank, performing significantly better than the phenotype-only model in UK Biobank (DeLong p=1.05x10–⁷⁶). Higher polygenic risk quartiles were associated with increased incidence of type 2 diabetes in both cohorts (global 2 p
A two-stage machine learning framework that integrates genetic and clinical information can support personalised screening for type 2 diabetes across diverse populations. The approach demonstrated robust performance across cohorts and offers a practical structure for early risk identification.
The number of patients requiring wound care is increasing, placing a burden on healthcare institutions and clinicians. While negative pressure wound therapy (NPWT) use has become increasingly common, Middle East-specific wound care guidelines are limited. An in-person meeting was held in Dubai with 15 wound care experts to develop guidelines for NPWT and NPWT with instillation and dwell (NPWTi-d) use for the Middle East. A literature search was performed using PubMed, Science Direct and Cochrane Reviews. Prior to the meeting, panel members reviewed literature and existing guidelines on NPWT and/or NPWTi-d use. A wound management treatment algorithm was created. Patient and wound assessment at presentation and throughout the treatment plan was recommended. Primary closure was recommended for simple wounds, and NPWT use was suggested for complex wounds requiring wound bed preparation. NPWTi-d use was advised when wound cleansing is required, if the patient is unsuitable for surgical debridement, or if surgical debridement is delayed. When NPWTi-d is unavailable, panel members recommended NPWT. Panel members recommended NPWT for wound bed preparation and NPWTi-d when wound cleansing is needed. These recommendations provide general guidance for NPWT and NPWTi-d use and should be updated as more clinical evidence becomes available.
Nurse managers are pivotal to the successful implementation of evidence-based practice (EBP). However, enhancing their skills and competencies remains a critical priority. Assessing the influence of nurse managers' competencies in managing and practicing EBP is essential, as it directly impacts outcomes across all levels of healthcare institutions.
This study explored how leadership, organizational support, and knowledge management influence EBP implementation among nurse managers.
A descriptive correlational study was conducted with a convenience sample of nurse managers in seven Egyptian hospitals. A total of 369 nurse managers completed three validated instruments: EBP Leadership and Organizational Support Scale (EBPLOSS), Knowledge Management Competencies for Nurse Managers (KMQN), and EBP Questionnaire (EBPQ). Descriptive statistics, hierarchical regression, and structural equation modeling (SEM) were applied for data analysis.
Nurse managers reported high levels of perceived EBP leadership (84.7%), organizational support (79.52%), knowledge management (KM) competencies (75.15%), and EBP implementation (74.83%). SEM analysis identified KM competencies as the strongest predictor of EBP implementation, with a direct effect (B = 0.86, p < 0.001) accounting for 86% of the total effect. EBP leadership significantly influenced EBP implementation both directly (β = 0.31, p = 0.02) and indirectly through KM competencies (B = 0.89, p < 0.001). Organizational support showed a minimal direct effect (B = 0.13, p < 0.05) and a slightly negative indirect effect through KM competencies (B = −0.10, p < 0.001).
KM competencies are critical for EBP implementation, mediating the effects of leadership and organizational support. Healthcare organizations should enhance nurse managers' KM skills, foster transformational leadership, and create supportive environments. Future research should address barriers and explore longitudinal relationships in EBP implementation from a managerial perspective.
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